AGI Can Make Smart Cities Even Smarter
The city is not just a collection of buildings, neighbourhoods, and roads.
It is a “living thing,” ever-changing, constantly growing and re-forming. Its population – both numbers and composition – is constantly changing, which means that the services residents need have to constantly be adjusted. As cities have gotten bigger and more complicated, administrators have increased the use of technology to manage them – and that includes utilizing artificial intelligence to manage trash collection, traffic flow, energy usage, and much more. AI turns cities into Smart Cities, enabling administrators to manage them much more effectively and efficiently.
But machine-learning based AI can only do so much. Like any living organism, cities change on a moment-to-moment basis, and by the time administrators parse the gathered data, the policy changes they might implement based on that data could easily be outdated or irrelevant. And because machine learning-based AI analysis systems need previous data in order to learn how to cope with a situation, sudden new events, like an unprecedented storm, mean that machine learning-based AI systems may not provide much insight into how to cope with the situation.
In order to manage situations like that – as well as the countless other novel issues that can crop up at any time – city administrators should step up their AI implementation, deploying systems that are capable of Artificial General Intelligence. Unlike standard machine learning-based systems, which analyze data in terms of patterns discovered once-and-for-all during a singular training phase, AGI systems continue building and developing those patterns on the fly, while ensuring that the patterns they create “make sense.” Thus, as new data is input into the system, AGI-based systems can adjust their understanding of the new situation – and respond accordingly.
Goals are described in a specialized format used by the AGI system to make its decisions. In a smart city scenario, the goals could include dozens of parameters, such as preventing traffic jams, ensuring efficient trash collection, deploying of police at specific times and places, identifying available housing units, providing insights into usage of electricity and other resources, pedestrian traffic in specific areas, the most opportune times to shop or commute to work, and many more. The AGI controller considers all the resources available and matches them to goals as they arise – while constantly learning based on results how best to meet the goals. As it learns, the controller develops a plan laying out the specific steps needed to achieve its goals. The advantage in an AGI system is that if the model and/or goals change in real-time, the controller can adjust its use of resources in order to ensure that the goals are met. The owner can change the description of the goals at any time, with AGI capable of “work on command.
